1. Field of the Invention
This invention relates to the arts of imaging and image processing, including sensor design and moiré reduction technologies.
2. Background of the Invention
Well-known imaging technologies include film (e.g. analog), digital, and analog-digital hybrid approaches. Film imaging processes use a set of lenses to focus an image onto a film sheet which is impregnated with grains of material reactive to the spectrum to be recorded, such as visible light, infrared (“IR”), or X-ray. The grains are randomly arranged in each sheet of film, and thus reproduction of the image on the developed film has a certain resolution based on the size and density of these grains.
In digital imaging, a sensor of uniformly-arranged sensing elements is used to capture “bits” or pixels of the image. Turning to FIG. 1, the system components (10) of a flatbed scanner are shown, which are commonly used to scan photographs and other analog images into digital data files. In this example, a document (1) is imaged using a one-row wide reflective element (3) such as a thin mirror, which is moved lengthwise (e.g. column-wise) (4, 4′) along the length of the document (1) in discrete steps by a stepper motor drive (2). At each step along the length of the document, a single row of image data is captured by a linear sensor array (7) upon which an image from the document is focused by a lens (6) via an optical path from the document, across the mirror (3), through the lens, and to the sensor (7). Upon conclusion of the scan (e.g. all lengthwise steps have been imaged), the sample date from the sensor is stored in a data file which can then be used for further image processing or reproduction.
Turning to FIG. 2, more details of a typical linear sensor array (7) are shown. The sensor elements are uniformly spaced at distance d1 from each other, or “linearly” spaced according to the function:Position of Sensor n=Pn=(n−1)·d1                 where the array is comprised of N sensors, n is the sensor number ranging from 1 through N, and d1 is the uniform distance between the sensors. These sensors may in practice be reactive to any range of electromagnetic (“EM”) spectrum according to the desired application, such as charge-coupled devices (“CCD”) for visible or IR imaging.        
Typically, the voltage level on each sensor element is measured and converted to a digital value relative to the amount of electromagnetic energy incident on the sensor element. Conversion to digital values are typically performed using an analog-to-digital converter having sufficient resolution (e.g. data width) for the intended application.
Additionally, mechanical and/or chemical filtering and band separation of the EM spectrum may be performed to produce “separated” data sets, such as use of color wheel or dyes on the elements to generate color-separated data sets.
As the stepper motor moves the mirror in even increments, a 2-dimensional array of image data is accumulated throughout the process representing rows (x-axis) and columns (y-axis) of data points which represent uniformly and evenly spaced samples of the original image.
Moiré patterns are artifacts of certain imaging processes which are perceptible to the human eye, but do not represent actual features or details in the original item imaged. They often resemble crosshatch halftones across all or a portion of an image. For imaging processes in which the original is an analog image, for example a photographic subject, moiré patterns may appear when certain features align with the sensors in the sensor array. For example, a digital photograph of a bug screen on a window often produces noticeable moiré patterns. Moiré patterns are most common when a new digital image is created from a digital (e.g. pixelated) source, such as scanning a newspaper photograph with a computer scanner. In this case, the resulting apparent pattern is actually an interference pattern between the physical spacing of the pixels of the original image and the spacing of the sensor array.
Just as in the phenomena of interference patterns between other types of signals, visual moiré patterns may become apparent at “harmonics” or integral multiples of spacing distances of the original image dots and the spacing distances of the sensor array. For example, if a digital image from a newspaper having a dot spacing of 600 dots per inch (“DPI”) is scanned with a 600 DPI scanner, a moiré pattern will likely appear in the new image. Doubling the resolution of the scanner to 1200 DPI will not avoid the problem because 1200 DPI represents a physical harmonic of 600 DPI, nor would undersampling the image at 300 DPI.
Many techniques have been developed to try to reduce moiré patterns in existing images, such as application of image processing such as Gaussian blurring, “descreening” algorithms, and “de-speckle” processes. Most of these have a result of reducing the sharpness of the overall image because they reduce the moiré pattern by spreading energy or brightness from a given pixel to adjacent pixels. For example, turning to FIG. 3a, pixel N represents a pixel of a moiré pattern in a single row or column, and in this case, a pattern which is darker than the surrounding pixels, N−1 and N+1 . The energy E2 of pixel N is lower than the energy E3 of the adjacent pixels N+1 and N−1 . A blurring process applies a partial or weighted averaging among regional or adjacent pixels, such as shown in FIG. 3b, wherein the energy of the pixel in the Moiré pattern is slightly increased to E2′, and the energy of the adjacent pixels are slightly decreased to E3′.
While this oftentimes decreases the obviousness of the moiré pattern to the human observer, it also reduces the “sharpness” or level of apparent detail of the entire image. If it is applied manually on a regional basis, the degradation to the entire image may be avoided, but the local areas are still degraded and substantial human intervention may be required to do so. Additionally, “edge effects” may become perceptible where the region of processing meets a region of unprocessed image. So, to date, most post processing attempts to reduce moiré patterns either result in image degradation, require substantial human operator effort, or both to some degree.
A common technique employed to avoid the generation of moiré patterns in the imaging process is to dither the sensor array such that the array is moved in physical position with respect to the original being “scanned” or imaged. In FIG. 4, such a dithering scanner with a one-dimensional sensor array (7) is shown. A mechanical jitter drive (40) is coupled to the array (7) such that it's x-axis position is varied slightly over time, usually in a sinusoidal or triangular pattern (41). This allows the array (7) to scan a pattern of points which are not simply an array of rows and columns, but which represent positions relative to the dithering function Px′(t). As such, fewer original image sources will have an interference pattern with the dithered sensor pattern, but it is still possible that portions of the original image source may interfere with the sensor pattern to cause localized moiré patterns. Additionally, such dithering mechanisms tend to add expense and failure rate to an assembly such as a flatbed scanner.
Therefore, there is a need in the art for a system and method which avoids generation of moiré patterns in digital images without the use of mechanical dithering mechanisms, intensive image post-processing technologies, or a high degree of human operator manipulation and editing. Further, there is a need in the art for this new system and method to maintain image quality, while being readily realizable using current sensor technology, and to preferrably be compatible with widely-used image compression and decompression technologies such as bitmap, JPEG (joint photographic experts) and MPEG image products.